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📦 Segmentation Gym

• Gym is for training, evaluating, and deploying deep learning models for image segmentation

• We take transferability seriously; Gym is designed to be a "one stop shop" for image segmentation on N-D imagery (i.e. any number of coincident bands, where a band is a 2D image at a certain image wavelength). It is tailored to Earth Observation and aerial remote sensing imagery, using monochrome, multispectral, and hyperspectral data.

  • Gym encodes relatively powerful models like UNets, and provides lots of ways to manipulate data, model training, and model architectures that should yield good results with some informed experimentation
  • Gym implements models based on the U-Net. Despite being one of the "original" deep learning segmentation models (dating to 2016), UNets have proven themselves enormously flexible for a wide range of image segmentation tasks and spatial regression tasks in the natural sciences. So, we expect these models, and, perhaps more importantly, the training and implementation of those models in an end-to-end pipeline, to work for a very wide variety of cases. Additional models may be added later.
  • You can read more about the models here but be warned! We at Gym HQ have discovered - often the hard way - that success is more about the data than the model. Gym helps you wrangle and tame your data, and makes your data work hard for you (nothing fancy, we just use augmentation)

• Gym works seamlessly with Doodler, a human-in-the loop labeling tool

• The number of classes present in the labels does not have a limit, and has the software has successfully been tested with 2 classes (i.e., binary classification) up to 12 classes.

• We have a paper that describes Gym using an example dataset. We also provide a detailed case study on a small demonstration dataset here. New users are STRONGLY RECOMMENDED to first run this test project to gain familiarity with the software and expected data formats, and also to test hardware for model training.